Photo by Adeniji Abdullahi A
As artificial intelligence grows more capable of imitating the aesthetic forms of humanity and begins to seep into multiple creative industries, music is now taking centre stage in the debate. For eons a site of emotional expression, storytelling, and cultural identity, music is now being made by algorithms that are not privy to the human experience. With tools like Suno and Udio generating fully formed tracks in seconds, and AI-produced songs by the likes of The Velvet Sundown racking up millions of streams, the sinister reality is that what was once an imaginative speculation of science fiction now exists in the material world: we can no longer wonder whether AI will be able to make music that sounds human. It does. What is up for debate now is how we choose to navigate it.
While artificial intelligence can replicate genre tropes and stylistic patterns with uncanny precision, it cannot replicate experience. It cannot feel heartbreak, joy, grief, or longing. It cannot respond to its own memories or emotions. A protest song written by someone with lived experience of injustice carries a different weight than one built from data. A love ballad resonates differently when it comes from a place of experienced vulnerability. These are the ineffable experiential currents that give music its power to move us. So what does it mean if one is deeply moved by a song, only to discover that it was made by AI?
This tension is at the heart of a recent piece from Ohio University, which features insights from music industry expert Josh Antonuccio. He describes the current moment as a “new creative ecology,” one in which AI and human creativity are increasingly interwoven. For some artists, particularly those working with limited resources, this development is promising. AI tools can generate new ideas, simulate rare instruments, or act as collaborators in early-stage production. Used thoughtfully, they can expand creative possibilities rather than constrain them – as is the case in any creative industry.
Yet, Antonuccio also warns of the risks. As these tools become widely accessible, the volume of musical content explodes. In a saturated digital landscape, deeply personal or experimental work may be drowned out by an ever-expanding tide of algorithmically generated material.
A CNBC analysis applies a sharper economic lens. Their focus is less on the philosophical or even artistic implications and more on the commodification of sound. AI music is going viral. Millions of listeners are consuming tracks without knowledge or concern for their origin. In this context, questions of authorship or intention are bypassed in favour of metrics like streams, shares, and clicks. As long as the track sounds polished and aligns with mood-based playlists, its origin becomes irrelevant.
This opacity of authenticity becomes especially fraught when AI-generated music is not flagged or disclosed to listeners, and compounded by the presence of bots increasing numbers of fraudulent streams. While platforms like Deezer, which revealed in April that over 20,000 AI tracks are uploaded daily, and ROKK, which recently banned fully AI-generated content, are beginning to push back against it, there is currently no legal requirement for platforms to label AI-generated content.
This carries consequences. As music becomes increasingly commodified, its cultural function continues to change. In this ecosystem, music becomes a node in a feedback loop optimised for retention. One senses an unsettling symmetry: AI is trained on human behaviour, and human behaviour is increasingly shaped by AI-curated outputs. In this echo chamber, the difference between creation and simulation dissolves.
The philosopher Jean Baudrillard’s concept of the simulacrum feels eerily relevant: representations that are no longer tethered to any real origin are consumed as if authentic. As AI-generated music becomes more convincing, the distinction between simulation and reality begins to erode.
As unflagged AI-generated music floods our streaming platforms, profound philosophical questions about the nature of art arise: Can a song stand alone? If a song no longer requires a human mind or heart to bring it into being, can it still be called art? If it is enjoyed by the listener, does it matter who – or what – made it? One cannot help but recall Walter Benjamin’s observation on the loss of “aura” in the age of mechanical reproduction. The song produced by AI may be flawless in tone and structure, but where is its history? Where is its soul? As Antonuccio notes, what makes music meaningful is not only its sonic architecture, but its emotional provenance.
And yet, even without AI, it is worth asking: when would we have reached a point of creative saturation anyway? Much of human-made music is, by nature, derivative; shaped by influence, tradition, and personal taste. Is an AI model’s reconfiguration of thousands of previously human-made songs so different from a musician drawing from their own listening history? In both cases, the product is a kind of pastiche. However, the key distinction lies in intent and awareness. A songwriter may pay homage, rebel, or reinterpret. An AI model doesn’t “intend” anything. It simply reorganises inputs to produce a statistically likely output.
This is not a call to resist AI altogether — this is impossible. As mentioned above, when treated as a sketchpad or co-creator, AI can help unlock new forms of expression. This is simply a call to ponder the danger that lies in using it as a substitute for artistic intention, rather than a supplement to it.
The root of the issue appears to lie in our profit-driven economy, and the flattening effect of consumer culture on artistic distinction. If listeners and distributors continue to prioritise convenience over connection, the definition of music itself may change.
There is an undeniable tension at the core of this moment – not necessarily that between machine and maker, but rather the mirror that the machine holds up to its maker’s society. AI offers extraordinary affordances: faster production, broader accessibility, and the ability to democratise tools that were once out of reach. But our lust for convenience and demand for content means that it also risks flooding the digital soundscape with noise, making it more difficult than ever for unique, authentic voices to rise to the surface.
As we enter this new era, authenticity may become the most valuable – and endangered – resource in music’s algorithmic age.
